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| Research article summary (published 30 Dec 2001): |
Automated sleep stage detection with a classical and a neural learning algorithm--methodological aspects.
Full Abstract
For classification tasks in biosignal processing, several strategies and algorithms can be used. Knowledge-based systems allow prior knowledge about the decision process to be integrated, both by the developer and by self-learning capabilities. For the classification stages in a sleep stage detection framework, three inference strategies were compared regarding their specific strengths:
a classical signal processing approach, artificial neural networks and neuro-fuzzy systems. Methodological aspects were assessed to attain optimum performance and maximum transparency for the user. Due to their effective and robust learning behavior, artificial neural networks could be recommended for pattern recognition, while neuro-fuzzy systems performed best for the processing of contextual information.
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Author information
Author/s: Schwaibold, M (M); Schöchlin, J (J); Bolz, A (A);
Affiliation: FZI Forschungszentrum Informatik, Karlsruhe, Germany. schwaibold@fzi.de
Journal and publication information
Publication Type: Comparative Study; Journal Article
Journal: Biomedizinische Technik. Biomedical engineering (Biomed Tech (Berl)), published in Germany. (Language: eng)
Reference: 2002-; vol 47 Suppl 1 Pt 1 (issue ) : pp 318-20
Dates: Created 2002/11/27; Completed 2003/03/06; Revised 2006/11/15;
PMID: 12451852, status: MEDLINE (last retrieval date: 11/6/2008)
Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.
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